@inproceedings{769,
  keywords = {Learning with constraints, local loss, global loss},
  author = {Wenting Qi and Charalampos Chelmis},
  title = {Hybrid Loss for Hierarchical Multi–label Classification Network},
  abstract = {Machine learning models for hierarchical multilabel classification (HMC) typically achieve low accuracy. This is because such models need not only predict multiple labels for each data instance, but also ensure that predicted labels conform to a given hierarchical structure. Existing state-of the-art strategies for HMC decouple the learning process from ensuring that predicted labels reside in a path of the hierarchy, thus inevitably degrading the overall classification accuracy. To address this limitation, we propose a novel loss function, which enables a model to encode both a global perspective of the class hierarchy, as well local class-relationships in adjacent hierarchical levels, to ensure that predictions align with the class hierarchy, both during training and testing. We demonstrate the superiority of the proposed approach against multiple state-of-the-art methods for HMC on 20 real-world datasets.},
  year = {2023},
  chapter = {819},
  pages = {10},
  month = {12},
  publisher = {IEEE International Conference on Big Data},
  isbn = {979-8-3503-2445-7},
  url = {https://par.nsf.gov/biblio/10507192},
  doi = {10.1109/BigData59044.2023.10386341},
}